AIM: To identify the association of baseline GGT level and QRISK2 score among patients with biopsy-proven nonalcoholic fatty liver disease (NAFLD).
METHODS: This was a retrospective study involving 1535 biopsy-proven NAFLD patients from 10 Asian centers in 8 countries using data collected by the Gut and Obesity in Asia (referred to as "GO ASIA") workgroup. All patients with available baseline GGT levels and all 16 variables for the QRISK2 calculation (QRISK2-2017; developed by researchers at the United Kingdom National Health Service; https://qrisk.org/2017/; 10-year cardiovascular risk estimation) were included and compared to healthy controls with the same age, sex, and ethnicity. Relative risk was reported. QRISK2 score > 10% was defined as the high-CVD-risk group. Fibrosis stages 3 and 4 (F3 and F4) were considered advanced fibrosis.
RESULTS: A total of 1122 patients (73%) had complete data and were included in the final analysis; 314 (28%) had advanced fibrosis. The median age (interquartile range [IQR]) of the study population was 53 (44-60) years, 532 (47.4%) were females, and 492 (43.9%) were of Chinese ethnicity. The median 10-year CVD risk (IQR) was 5.9% (2.6-10.9), and the median relative risk of CVD over 10 years (IQR) was 1.65 (1.13-2.2) compared to healthy individuals with the same age, sex, and ethnicity. The high-CVD-risk group was significantly older than the low-risk group (median [IQR]: 63 [59-67] vs 49 [41-55] years; P < 0.001). Higher fibrosis stages in biopsy-proven NAFLD patients brought a significantly higher CVD risk (P < 0.001). Median GGT level was not different between the two groups (GGT [U/L]: Median [IQR], high risk 60 [37-113] vs low risk 66 [38-103], P = 0.56). There was no correlation between baseline GGT level and 10-year CVD risk based on the QRISK2 score (r = 0.02).
CONCLUSION: The CVD risk of NAFLD patients is higher than that of healthy individuals. Baseline GGT level cannot predict CVD risk in NAFLD patients. However, advanced fibrosis is a predictor of a high CVD risk.
METHOD: We estimated the two conditions for a Zika outbreak emergence in Southeast Asia: (i) the risk of Zika introduction from Latin America and the Caribbean and, (ii) the risk of autochthonous transmission under varying assumptions on population immunity. We also validated the model used to estimate the risk of introduction by comparing the estimated number of Zika seeds introduced into the United States with case counts reported by the Centers for Disease Control and Prevention (CDC).
RESULTS: There was good agreement between our estimates and case counts reported by the CDC. We thus applied the model to Southeast Asia and estimated that, on average, 1-10 seeds were introduced into Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam. We also found increasing population immunity levels from 0 to 90% reduced probability of autochthonous transmission by 40% and increasing individual variation in transmission further reduced the outbreak probability.
CONCLUSIONS: Population immunity, combined with heterogeneity in transmission, can explain why no large-scale outbreak was observed in Southeast Asia during the 2015-16 epidemic.
METHODS: A prospective pre- and post-intervention study was conducted among medical inpatients in a Malaysian secondary care hospital. DVT and bleeding risks were stratified using validated Padua Risk Assessment Model (RAM) and International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) Bleeding Risk Assessment Model. Pharmacist-driven DRAT was developed and implemented post-interventional phase. DVT prophylaxis use was determined and its appropriateness was compared between pre and post study using multivariate logistic regression with IBM SPSS software version 21.0.
RESULTS: Overall, 286 patients (n=142 pre-intervention versus n=144 post-intervention) were conveniently recruited. The prevalence of DVT prophylaxis use was 10.8%. Appropriate thromboprophylaxis prescribing increased from 64.8% to 68.1% post-DRAT implementation. Of note, among high DVT risk patients, DRAT intervention was observed to be a significant predictor of appropriate thromboprophylaxis use (14.3% versus 31.3%; adjusted odds ratio=2.80; 95% CI 1.01 to 7.80; p<0.05).
CONCLUSION: The appropriateness of DVT prophylaxis use was suboptimal but doubled after implementation of DRAT intervention. Thus, an integrated risk stratification checklist is an effective approach for the improvement of rational DVT prophylaxis use.
MATERIALS AND METHODS: We analysed retrospective data of chest pain patients presenting to ED HUSM from 1st June 2020 till 31st January 2021 based on the patient's history, ECG findings, risk factors, age and troponin level. The patients were stratified as low risk (MHS and HEAR score of 0-3), intermediate risk (MHS and HEAR score of 4-6), and high risk (MHS of 7-10 and HEAR score of 7-8). The association of the MHS and HEAR score with MACE at 6 weeks' time was evaluated using simple logistic regression.
RESULTS: This study included 147 patients in the MHS analysis and 71 patients in HEAR score analysis. The incident rate of MACE in low, intermediate and high risk was 0%,16.3%, and 34.7%, in the MHS group, and 0%, 3.22%, and 6.66% in HEAR score group. The mean difference between MACE and non-MACE in MHS and HEAR score groups was -2.29 (CI: -3.13,1.44, p<0.001) and -2.51(CI: -5.23, 0.21, p=0.070), respectively. There was no significant association between the incidence rate of MACE with modified HEART score (MHS) and HEAR score groups (p>0.95).
CONCLUSION: HEAR score is not feasible to be used as a risk stratification tool for chest pain patients presenting to ED HUSM in comparison to MHS. Further studies are required to validate the results.
METHODS: Data from the web-based CSR were collected for cataract surgery performed from 2008 to 2013. Data was contributed by 36 Malaysian Ministry of Health public hospitals. Information on patient's age, ethnicity, cause of cataract, ocular and systemic comorbidity, type of cataract surgery performed, local anaesthesia and surgeon's status was noted. Combined procedures and type of hospital admission were recorded. PCR risk indicators were identified using logistic regression analysis to produce adjusted OR for the variables of interest.
RESULTS: A total of 150 213 cataract operations were registered with an overall PCR rate of 3.2%. Risk indicators for PCR from multiple logistic regression were advancing age, male gender (95% CI 1.04 to 1.17; OR 1.11), pseudoexfoliation (95% CI 1.02 to 1.82; OR 1.36), phacomorphic lens (95% CI 1.25 to 3.06; OR 1.96), diabetes mellitus (95% CI 1.13 to 1.29; OR 1.20) and renal failure (95% CI 1.09 to 1.55; OR 1.30). Surgical PCR risk factors were combined vitreoretinal surgery (95% CI 2.29 to 3.63; OR 2.88) and less experienced cataract surgeons. Extracapsular cataract extraction (95% CI 0.76 to 0.91; OR 0.83) and kinetic anaesthesia were associated with lower PCR rates.
CONCLUSIONS: This study was agreed with other studies for the risk factors of PCR with the exception of local anaesthesia given and type of cataract surgery. Better identification of high-risk patients for PCR decreases intraoperative complications and improves cataract surgical outcomes.
MATERIALS AND METHOD: 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.
RESULTS: 166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.
CONCLUSIONS: Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.